Rear-End Crash Risk Analysis considering Drivers’ Visual Perception and Traffic Flow Uncertainty: A Hierarchical Hybrid Bayesian Network Approach
Rear-end crashes or crash risk is widely recognized as safety-critical state of vehicles under comprehensive conditions. This study investigated the association between traffic flow uncertainty, drivers’ visual perception, car-following behavior, roadway and vehicular characteristics, and rear-end c...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
|
| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2021/7028660 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849682992410132480 |
|---|---|
| author | Naikan Ding Linsheng Lu Nisha Jiao |
| author_facet | Naikan Ding Linsheng Lu Nisha Jiao |
| author_sort | Naikan Ding |
| collection | DOAJ |
| description | Rear-end crashes or crash risk is widely recognized as safety-critical state of vehicles under comprehensive conditions. This study investigated the association between traffic flow uncertainty, drivers’ visual perception, car-following behavior, roadway and vehicular characteristics, and rear-end crash risk variation and compared the crash risk variation prediction with and without specific flow-level data. Two datasets comprising 5055 individual vehicles in car-following state were collected through on-road experiments on two freeways in China. A hierarchical hybrid BN model approach was proposed to capture the association between drivers’ visual perception, traffic flow uncertainty, and rear-end crash risk variation. Results show that (1) the BN model with flow-level data outperformed the BN model without flow-level data and could predict 85.3% of the cases of crash risk decrease, with a false alarm rate of 21.4%; (2) the hierarchical hybrid BN models showed plausible spatial transferability in predicting crash risk variation; and (3) the incorporation of specific flow-level variables and data greatly benefited the successful identification of rear-end crash risk variations. The findings of this study suggest that rear-end crash risk is inherently associated with both individual driving behaviors and traffic flow uncertainty, and appropriate visual perceptual information could compensate for crash risk and improve safety. |
| format | Article |
| id | doaj-art-d214e17d7b394898bb5dd1920d11c45c |
| institution | DOAJ |
| issn | 1607-887X |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-d214e17d7b394898bb5dd1920d11c45c2025-08-20T03:24:02ZengWileyDiscrete Dynamics in Nature and Society1607-887X2021-01-01202110.1155/2021/7028660Rear-End Crash Risk Analysis considering Drivers’ Visual Perception and Traffic Flow Uncertainty: A Hierarchical Hybrid Bayesian Network ApproachNaikan Ding0Linsheng Lu1Nisha Jiao2School of Civil Engineering and ArchitectureSchool of Civil Engineering and ArchitecturePlanning Research OfficeRear-end crashes or crash risk is widely recognized as safety-critical state of vehicles under comprehensive conditions. This study investigated the association between traffic flow uncertainty, drivers’ visual perception, car-following behavior, roadway and vehicular characteristics, and rear-end crash risk variation and compared the crash risk variation prediction with and without specific flow-level data. Two datasets comprising 5055 individual vehicles in car-following state were collected through on-road experiments on two freeways in China. A hierarchical hybrid BN model approach was proposed to capture the association between drivers’ visual perception, traffic flow uncertainty, and rear-end crash risk variation. Results show that (1) the BN model with flow-level data outperformed the BN model without flow-level data and could predict 85.3% of the cases of crash risk decrease, with a false alarm rate of 21.4%; (2) the hierarchical hybrid BN models showed plausible spatial transferability in predicting crash risk variation; and (3) the incorporation of specific flow-level variables and data greatly benefited the successful identification of rear-end crash risk variations. The findings of this study suggest that rear-end crash risk is inherently associated with both individual driving behaviors and traffic flow uncertainty, and appropriate visual perceptual information could compensate for crash risk and improve safety.http://dx.doi.org/10.1155/2021/7028660 |
| spellingShingle | Naikan Ding Linsheng Lu Nisha Jiao Rear-End Crash Risk Analysis considering Drivers’ Visual Perception and Traffic Flow Uncertainty: A Hierarchical Hybrid Bayesian Network Approach Discrete Dynamics in Nature and Society |
| title | Rear-End Crash Risk Analysis considering Drivers’ Visual Perception and Traffic Flow Uncertainty: A Hierarchical Hybrid Bayesian Network Approach |
| title_full | Rear-End Crash Risk Analysis considering Drivers’ Visual Perception and Traffic Flow Uncertainty: A Hierarchical Hybrid Bayesian Network Approach |
| title_fullStr | Rear-End Crash Risk Analysis considering Drivers’ Visual Perception and Traffic Flow Uncertainty: A Hierarchical Hybrid Bayesian Network Approach |
| title_full_unstemmed | Rear-End Crash Risk Analysis considering Drivers’ Visual Perception and Traffic Flow Uncertainty: A Hierarchical Hybrid Bayesian Network Approach |
| title_short | Rear-End Crash Risk Analysis considering Drivers’ Visual Perception and Traffic Flow Uncertainty: A Hierarchical Hybrid Bayesian Network Approach |
| title_sort | rear end crash risk analysis considering drivers visual perception and traffic flow uncertainty a hierarchical hybrid bayesian network approach |
| url | http://dx.doi.org/10.1155/2021/7028660 |
| work_keys_str_mv | AT naikanding rearendcrashriskanalysisconsideringdriversvisualperceptionandtrafficflowuncertaintyahierarchicalhybridbayesiannetworkapproach AT linshenglu rearendcrashriskanalysisconsideringdriversvisualperceptionandtrafficflowuncertaintyahierarchicalhybridbayesiannetworkapproach AT nishajiao rearendcrashriskanalysisconsideringdriversvisualperceptionandtrafficflowuncertaintyahierarchicalhybridbayesiannetworkapproach |